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cs.LG2024
Learning Better Representations From Less Data For Propositional Satisfiability
Mohamed Ghanem, Frederik Schmitt, Julian Siber +1
Training neural networks on NP-complete problems typically demands very large amounts of training data and often needs to be coupled with computationally expensive symbolic verifie…
cs.LO2024★ 1 cited
NeuroSynt: A Neuro-symbolic Portfolio Solver for Reactive Synthesis
Matthias Cosler, Christopher Hahn, Ayham Omar +1
We introduce NeuroSynt, a neuro-symbolic portfolio solver framework for reactive synthesis. At the core of the solver lies a seamless integration of neural and symbolic approaches…